{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Introduction to Radar Using Python and MATLAB***\n",
    "## Andy Harrison - Copyright (C) 2019 Artech House\n",
    "<br/>\n",
    "\n",
    "# Power Aperture Product\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Referring to Equation 4.54\n",
    "\n",
    "\\begin{equation}\\label{eq:radar_equation_search_final}\n",
    "    {SNR}_o = \\frac{P_{av}\\, A_e\\, \\sigma }{(4\\pi)^3\\, k\\, T_0\\, F\\, L\\, r^4} \\, \\frac{T_{scan}}{\\Omega},\n",
    "\\end{equation}\n",
    "\n",
    "$P_{av}\\, A_e$ is the power aperture product and is a useful measure of performance for search and surveillance type radar systems.  \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begin by getting the library path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lib_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the minimum and maximum target range (m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_min_range = 10e3\n",
    "\n",
    "target_max_range = 100e3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the `linspace` routine from `scipy` and set up the target range array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import linspace\n",
    "\n",
    "target_range = linspace(target_min_range, target_max_range, 2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the noise figure (dB), the radar losses (dB), the target RCS (dBsm), the signal to noise ration (dB), the system temperature (K), the search volume (sr), and the scan time (s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.constants import pi\n",
    "\n",
    "noise_figure = 4.0\n",
    "\n",
    "losses = 9.0\n",
    "\n",
    "target_rcs = -5.0\n",
    "\n",
    "signal_to_noise = 23.0\n",
    "\n",
    "system_temperature = 305\n",
    "\n",
    "search_volume = 2.0 * pi\n",
    "\n",
    "scan_time = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the keyword args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "kwargs = {'target_range': target_range,\n",
    "\n",
    "           'system_temperature': system_temperature,\n",
    "\n",
    "                  'search_volume': search_volume,\n",
    "\n",
    "                  'noise_factor': 10 ** (noise_figure / 10.0),\n",
    "\n",
    "                  'losses': 10 ** (losses / 10.0),\n",
    "\n",
    "                  'signal_to_noise': 10 ** (signal_to_noise / 10.0),\n",
    "\n",
    "                  'scan_time': scan_time,\n",
    "\n",
    "                  'target_rcs': 10 ** (target_rcs / 10.0)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the `power_aperture` routine from `search_radar_range`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Libs.radar_range.search_radar_range import power_aperture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the power aperture product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    " power_aperture = power_aperture(**kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the `matplotlib` routines and the `log10` routine for plotting the power aperture product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from numpy import log10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 10)\n",
    "\n",
    "\n",
    "# Display the results\n",
    "\n",
    "plt.plot(target_range / 1.0e3, 10.0 * log10(power_aperture), '')\n",
    "\n",
    "\n",
    "\n",
    "# Set the plot title and labels\n",
    "\n",
    "plt.title('Power Aperture Product', size=14)\n",
    "\n",
    "plt.xlabel('Target Range (km)', size=14)\n",
    "\n",
    "plt.ylabel('Power Aperture (dB)', size=14)\n",
    "\n",
    "\n",
    "\n",
    "# Set the tick label size\n",
    "\n",
    "plt.tick_params(labelsize=12)\n",
    "\n",
    "\n",
    "\n",
    "# Turn on the grid\n",
    "\n",
    "plt.grid(linestyle=':', linewidth=0.5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
